Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel
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ORIGINAL ARTICLE
Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot Tarmizi Ahmad Izzuddin1,2
•
Norlaili Mat Safri2 • Mohd Afzan Othman2
Received: 21 April 2020 / Accepted: 24 September 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with a shared control scheme. In general, subjects wearing a singlechannel EEG electrode located at F8 (10–20 international standards) were required to perform mental tasks by mentally visualising the rotation of a star and mind relaxation at a specific time and by robot orientation. The visualisation of a rotating star suggests that the mobile robot is currently oriented towards a target, thus enabling target selection. We showed that proposed classifier obtained the best accuracy of 92.09% in classifying the subject’s performing mental rotation task or mental relaxation when compared with conventional classification methods such as support vector machine—75.69%, Kthnearest neighbour—65.50% and linear discriminant analysis—65.20%. Furthermore, different from conventional methods, the use of 1D-CNN enables end-to-end learning, that is the automatic decoding of EEG signals without requiring feature selection or extraction. To validate that the proposed classifier performs better than conventional methods, the extracted kernel weights of proposed 1D-CNN filters were visualised as a temporal plot, and spectral analysis was performed on the extracted weights. The obtained results confirmed that the proposed 1D-CNN was able to generate filters that resemble the EEG wave patterns of different frequencies and spectral analysis confirmed that the filters exploited information from multiple frequency bands (such as alpha band and beta band) that are often associated with a heightened mental state when performing mental tasks. Keywords Brain–computer interface (BCI) Convolutional neural network (CNN) Electroencephalogram (EEG) Mental imagery
1 Introduction & Norlaili Mat Safri [email protected] Tarmizi Ahmad Izzuddin [email protected] Mohd Afzan Othman [email protected] 1
Department of Control, Instrumentation and Automation, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia
2
Department of Electronic and Computer Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
It is known that mental imagery is one of the primary mental human events that allow us to remember, plan for the future, navigate and make decisions. It can be defined as ‘‘pictures in mind’’ or visual representation in the absence of environmental input [1]. This state of mind can be evoked by performing certain cognitive tasks such as the
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